Merge situation exposure algorithms to maximize exposure time

ABSTRACT

A method of improving a merging efficiency of an ego vehicle is described. The method includes determining one or more merge gaps between vehicles in a target lane of a multilane highway. The method also includes computing an exposure time in which the ego vehicle is specified to merge into the one or merge gaps. The method further includes selecting a merge gap between a first vehicle and a second vehicle in the target lane of the multilane highway having a maximum exposure time.

BACKGROUND Field

Certain aspects of the present disclosure generally relate to autonomousvehicle technology and, more particularly, to merge situation exposurealgorithms to maximize exposure Time.

Background

Autonomous agents (e.g., vehicles, robots, etc.) rely on machine visionfor sensing a surrounding environment by analyzing areas of interest ina scene from images of the surrounding environment. Although scientistshave spent decades studying the human visual system, a solution forrealizing equivalent machine vision remains elusive. Realizingequivalent machine vision is a goal for enabling truly autonomousagents. Machine vision, however, is distinct from the field of digitalimage processing. In particular, machine vision involves recovering athree-dimensional (3D) structure of the world from images and using the3D structure for fully understanding a scene. That is, machine visionstrives to provide a high-level understanding of a surroundingenvironment, as performed by the human visual system.

Autonomous agents, such as driverless cars and robots, are quicklyevolving and have becoming a reality in this decade. Because autonomousagents have to interact with humans, however, many critical concernsarise. For example, how to design vehicle control of an autonomousvehicle using machine learning. Unfortunately, vehicle control bymachine learning is less effective in complicated traffic environmentsinvolving complex interactions between vehicles (e.g. a situation wherea controlled (ego) vehicle merges/changes onto/into a traffic lane).

Machine learning techniques for vehicle control using a network toselect a vehicle control action for an ego vehicle are desired. Forexample, a selected speed/acceleration/steering angle of the controlled(ego) vehicle may be applied as a vehicle control action. Theseconventional machine learning techniques do not consider a temporalcomponent of a current traffic state for selecting vehicle controlactions.

SUMMARY

A method of improving a merging efficiency of an ego vehicle isdescribed. The method includes determining one or more merge gapsbetween vehicles in a target lane of a multilane highway. The methodalso includes computing an exposure time in which the ego vehicle isspecified to merge into the one or merge gaps. The method furtherincludes selecting a merge gap between a first vehicle and a secondvehicle in the target lane of the multilane highway having a maximumexposure time.

A non-transitory computer-readable medium having program code recordedthereon for improving a merging efficiency of an ego vehicle isdescribed. The program code is executed by a processor. Thenon-transitory computer-readable medium includes program code todetermine one or more merge gaps between vehicles in a target lane of amultilane highway. The non-transitory computer-readable medium alsoincludes program code to compute an exposure time in which the egovehicle is specified to merge into the one or merge gaps. Thenon-transitory computer-readable medium further includes program code toselect a merge gap between a first vehicle and a second vehicle in thetarget lane of the multilane highway having a maximum exposure time.

A system to improve merging efficiency of an ego vehicle is described.The system includes a vehicle perception module. The vehicle perceptionmodule includes a convolutional neural network configured to determineone or more merge gaps between vehicles in a target lane of a multilanehighway. The system also includes a controller module configured tocompute an exposure time in which the ego vehicle is specified to mergeinto one of the one or merge gaps. The controller module is alsoconfigured to select a merge gap between a first vehicle and a secondvehicle in the target lane of the multilane highway having a maximumexposure time.

This has outlined, rather broadly, the features and technical advantagesof the present disclosure in order that the detailed description thatfollows may be better understood. Additional features and advantages ofthe present disclosure will be described below. It should be appreciatedby those skilled in the art that the present disclosure may be readilyutilized as a basis for modifying or designing other structures forcarrying out the same purposes of the present disclosure. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the teachings of the present disclosureas set forth in the appended claims. The novel features, which arebelieved to be characteristic of the present disclosure, both as to itsorganization and method of operation, together with further objects andadvantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a neuralnetwork using a system-on-a-chip (SOC) for a vehicle behavior controlsystem, in accordance with aspects of the present disclosure.

FIG. 2 is a block diagram illustrating a software architecture that maymodularize artificial intelligence (AI) functions for a vehicle behaviorcontrol system of an autonomous agent, according to aspects of thepresent disclosure.

FIG. 3 is a diagram illustrating an example of a hardware implementationfor a vehicle behavior control system, according to aspects of thepresent disclosure.

FIG. 4 is a diagram illustrating an overview of a highway environment,including vehicles on a highway main-lane and a controlled ego vehicleon a highway on-ramp, according to aspects of the present disclosure.

FIG. 5 is a diagram illustrating an overview of a traffic environment,including vehicles on highway lanes and a controlled ego vehicle priorto a lane change, according to aspects of the present disclosure.

FIG. 6 is a flowchart illustrating a method of improving a mergingefficiency of an ego vehicle, according to aspects of the presentdisclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. It will be apparent tothose skilled in the art, however, that these concepts may be practicedwithout these specific details. In some instances, well-known structuresand components are shown in block diagram form in order to avoidobscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the present disclosure is intended to cover any aspect ofthe present disclosure, whether implemented independently of or combinedwith any other aspect of the present disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth. In addition, the scope of the presentdisclosure is intended to cover such an apparatus or method practicedusing other structure, functionality, or structure and functionality inaddition to, or other than the various aspects of the present disclosureset forth. It should be understood that any aspect of the presentdisclosure disclosed may be embodied by one or more elements of a claim.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the presentdisclosure. Although some benefits and advantages of the preferredaspects are mentioned, the scope of the present disclosure is notintended to be limited to particular benefits, uses, or objectives.Rather, aspects of the present disclosure are intended to be broadlyapplicable to different technologies, system configurations, networksand protocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of the presentdisclosure, rather than limiting the scope of the present disclosurebeing defined by the appended claims and equivalents thereof.

Traffic congestion on highways affects millions of people and presentsan urgent problem to solve. In particular, vehicles at highway mergingsections (e.g., such as on-ramp and land-drop bottlenecks) perform lanechanges, which may generate traffic oscillations and extra congestion.Both main-lane and on-ramp traffic are potentially congested due toirregular lane change behavior and unexpected braking maneuvers ofsurrounding vehicles. Automated vehicles are expected to reduce trafficaccidents and improve traffic efficiency. In particular, automation ofvehicle control on highways is rapidly advancing, which may eventuallyreduce traffic accidents and improve traffic efficiency.

Reducing traffic congestion may be achieved by effectively directingtiming and speed of controlled vehicles. For example, the timing andspeed of vehicles is controlled when merging onto main-lane traffic in amanner that does not detrimentally affect traffic in the highwaymain-lane as well as the highway on-ramp. According to one aspect of thepresent disclosure, a vehicle merge control system is described toeffectively merge a controlled vehicle onto a highway main-lane (orchange lanes on the highway) while reducing the traffic impact on thehighway main-lane and on-ramp.

Vehicle control by machine learning is less effective in complicatedtraffic environments. For example, these traffic environments mayinvolve complex interactions between vehicles, including situationswhere a controlled (ego) vehicle merges onto a traffic lane.Conventional machine learning techniques for vehicle control may use anetwork to select an appropriate vehicle control action from input datarelative to the ego vehicle. For example, a selectedspeed/acceleration/steering angle of the controlled (ego) vehicle may beapplied as a vehicle control action to enter a merge gap. As describedherein, a “merge gap” is a gap located between two vehicles into which athird vehicle may desire to merge.

Unfortunately, conventional machine learning techniques do not considera temporal component of a current traffic state for selecting vehiclecontrol actions. For example, conventional machine learning techniquesdoes not view a merge gap as an exposure time. Rather, conventionalmachine learning techniques view a “merge gap” as an open space withouta time component. Aspects of the present disclosure provide animprovement over conventional machine learning techniques by consideringa merge gap as an exposure time. This aspects of the present disclosureseeks to maximize the exposure time associated with a merge gap,allowing an ego vehicle to more easily enter a stream of traffic.

Aspects of the present disclosure add a merge gap temporal estimationtask to improve a vehicle merge control system. This aspect of thepresent disclosure uses data from a perception system of the ego vehicleand/or data received from other connected vehicles (e.g., viavehicle-to-vehicle (V2V) communication in connected vehicleenvironments). Using this data, the vehicle merge control systemdetermines the presence of any “merge gaps” that are located betweenvehicles in a stream of traffic. In addition, the vehicle merge controlsystem considers the speed of the vehicles forming the merge gap and thespeed of the ego vehicle. Based on this information, the vehicle mergecontrol system seeks to maximize the period of time that an ego vehiclehas to merge into the merge gap. As described herein, this period oftime may be referred to as an exposure time.

According to aspects of the present disclosure, the vehicle mergecontrol system seeks to maximize the exposure time in order for an egovehicle to make a more fluid entry into a stream of traffic. The streamof traffic can include any stream of traffic, but generally involvesentrance ramps to highways. As such, the stream of traffic is on thehighway in the ego vehicle is attempting to get into the stream oftraffic via the entrance ramp. The vehicle merge control system may bemounted within a vehicle and include a processor in communication with avehicle perception system. The vehicle perception system can include anumber of different sensors, such as cameras, a light detection andranging (LiDAR) sensor or a radio detection and ranging (RADAR) sensor,sonar, or other like sensor. Additionally, the vehicle merge controlsystem may be able to communicate with other connected vehicles usingvehicle-to-vehicle communication.

FIG. 1 illustrates an example implementation of the aforementionedsystem and method for a vehicle behavior control system using asystem-on-a-chip (SOC) 100 of an autonomous vehicle 150. The SOC 100 mayinclude a single processor or multi-core processors (e.g., a centralprocessing unit (CPU) 102), in accordance with certain aspects of thepresent disclosure. Variables (e.g., neural signals and synapticweights), system parameters associated with a computational device(e.g., neural network with weights), delays, frequency bin information,and task information may be stored in a memory block. The memory blockmay be associated with a neural processing unit (NPU) 108, a CPU 102, agraphics processing unit (GPU) 104, a digital signal processor (DSP)106, a dedicated memory block 118, or may be distributed across multipleblocks. Instructions executed at a processor (e.g., CPU 102) may beloaded from a program memory associated with the CPU 102 or may beloaded from the dedicated memory block 118.

The SOC 100 may also include additional processing blocks configured toperform specific functions, such as the GPU 104, the DSP 106, and aconnectivity block 110, which may include fifth generation (5G) cellularnetwork technology, fourth generation long term evolution (4G LTE)connectivity, unlicensed WiFi connectivity, USB connectivity, Bluetooth®connectivity, and the like. In addition, a multimedia processor 112 incombination with a display 130 may, for example, apply a temporalcomponent of a current traffic state to select a vehicle behaviorcontrol action, according to the display 130 illustrating a view of avehicle. In some aspects, the NPU 108 may be implemented in the CPU 102,DSP 106, and/or GPU 104. The SOC 100 may further include a sensorprocessor 114, image signal processors (ISPs) 116, and/or navigation120, which may, for instance, include a global positioning system.

The SOC 100 may be based on an Advanced Risk Machine (ARM) instructionset or the like. In another aspect of the present disclosure, the SOC100 may be a server computer in communication with the autonomousvehicle 150. In this arrangement, the autonomous vehicle 150 may includea processor and other features of the SOC 100. In this aspect of thepresent disclosure, instructions loaded into a processor (e.g., CPU 102)or the NPU 108 of the autonomous vehicle 150 may include program code todetermine one or more merge gaps between vehicles in a target lane of amultilane highway based on images processed by the sensor processor 114.The instructions loaded into a processor (e.g., CPU 102) may alsoinclude program code to compute an exposure time in which the egovehicle is specified to merge into the one or merge gaps, and programcode to select a merge gap between a first vehicle and a second vehiclein the target lane of the multilane highway having a maximum exposuretime.

FIG. 2 is a block diagram illustrating a software architecture 200 thatmay modularize artificial intelligence (AI) functions for selecting avehicle control action of an autonomous agent using a temporal componentof a current traffic state, according to aspects of the presentdisclosure. Using the architecture, a controller application 202 may bedesigned such that it may cause various processing blocks of an SOC 220(for example a CPU 222, a DSP 224, a GPU 226, and/or an NPU 228) toperform supporting computations during run-time operation of thecontroller application 202. While FIG. 2 describes the softwarearchitecture 200 for selecting a vehicle control action of an autonomousagent, it should be recognized that vehicle action control functionalityis not limited to autonomous agents. According to aspects of the presentdisclosure, vehicle action control functionality is applicable to anyvehicle type, provided the vehicle is equipped with appropriatefunctions (e.g., vehicle-to-vehicle (V2V) communication) of connectedvehicle applications and/or an advanced driver assistance system (ADAS).

The controller application 202 may be configured to call functionsdefined in a user space 204 that may, for example, provide for vehicleaction control services. The controller application 202 may make arequest to compile program code associated with a library defined in atemporal traffic state application programming interface (API) 206 toperform a vehicle behavior action control selection. Selection of thevehicle behavior control action may ultimately rely on the output of aconvolutional neural network configured to select a vehicle controlaction of an autonomous agent using a temporal component of a currenttraffic state.

A run-time engine 208, which may be compiled code of a runtimeframework, may be further accessible to the controller application 202.The controller application 202 may cause the run-time engine 208, forexample, to take actions for controlling the autonomous agent. When anego vehicle intends to merge onto a traffic lane, the run-time engine208 may in turn send a signal to an operating system 210, such as aLinux Kernel 212, running on the SOC 220. FIG. 2 illustrates the LinuxKernel 212 as software architecture for implementing control of anautonomous agent using temporal traffic state information. It should berecognized, however, aspects of the present disclosure are not limitedto this exemplary software architecture. For example, other kernels maybe used to provide the software architecture to support vehicle controlaction selection functionality.

The operating system 210, in turn, may cause a computation to beperformed on the CPU 222, the DSP 224, the GPU 226, the NPU 228, or somecombination thereof. The CPU 222 may be accessed directly by theoperating system 210, and other processing blocks may be accessedthrough a driver, such as drivers 214-218 for the DSP 224, for the GPU226, or for the NPU 228. In the illustrated example, the deep neuralnetwork may be configured to run on a combination of processing blocks,such as the CPU 222 and the GPU 226, or may be run on the NPU 228, ifpresent.

FIG. 3 is a diagram illustrating an example of a hardware implementationfor a vehicle behavior control system 300, according to aspects of thepresent disclosure. The vehicle behavior control system 300 may beconfigured for improved merging efficiency of an ego vehicle. Thevehicle behavior control system 300 may be a component of a vehicle, arobotic device, or other non-autonomous device (e.g., non-autonomousvehicles, ride-share cars, etc.). For example, as shown in FIG. 3, thevehicle behavior control system 300 is a component of a car 350.

Aspects of the present disclosure are not limited to the vehiclebehavior control system 300 being a component of the car 350. Otherdevices, such as a bus, motorcycle, or other like non-autonomousvehicle, are also contemplated for implementing the vehicle behaviorcontrol system 300. In this example, the car 350 may be autonomous orsemi-autonomous; however, other configurations for the car 350 arecontemplated, such as an advanced driver assistance system (ADAS).

The vehicle behavior control system 300 may be implemented with aninterconnected architecture, represented generally by an interconnect336. The interconnect 336 may include any number of point-to-pointinterconnects, buses, and/or bridges depending on the specificapplication of the vehicle behavior control system 300 and the overalldesign constraints. The interconnect 336 links together various circuitsincluding one or more processors and/or hardware modules, represented bya sensor module 302, a vehicle behavior controller 310, a processor 320,a computer-readable medium 322, a communication module 324, a plannermodule 326, a locomotion module 328, an onboard unit 330, and a locationmodule 340. The interconnect 336 may also link various other circuitssuch as timing sources, peripherals, voltage regulators, and powermanagement circuits, which are well known in the art, and therefore,will not be described any further.

The vehicle behavior control system 300 includes a transceiver 332coupled to the sensor module 302, the vehicle behavior controller 310,the processor 320, the computer-readable medium 322, the communicationmodule 324, the planner module 326, the locomotion module 328, thelocation module 340, and the onboard unit 330. The transceiver 332 iscoupled to antenna 334. The transceiver 332 communicates with variousother devices over a transmission medium. For example, the transceiver332 may receive commands via transmissions from a user or a connectedvehicle. In this example, the transceiver 332 may receive/transmitvehicle-to-vehicle traffic state information for the vehicle behaviorcontroller 310 to/from connected vehicles within the vicinity of the car350.

The vehicle behavior control system 300 includes the processor 320coupled to the computer-readable medium 322. The processor 320 performsprocessing, including the execution of software stored on thecomputer-readable medium 322 to provide functionality according to thedisclosure. The software, when executed by the processor 320, causes thevehicle behavior control system 300 to perform the various functionsdescribed for vehicle behavior control (e.g., vehicle merging control)of the car 350, or any of the modules (e.g., 302, 310, 324, 326, 328,330, and/or 340). The computer-readable medium 322 may also be used forstoring data that is manipulated by the processor 320 when executing thesoftware.

The sensor module 302 may obtain measurements via different sensors,such as a first sensor 306 and a second sensor 304. The first sensor 306may be a vision sensor (e.g., a stereoscopic camera or a red-green-blue(RGB) camera) for capturing 2D images. The second sensor 304 may be aranging sensor, such as a light detection and ranging (LiDAR) sensor ora radio detection and ranging (RADAR) sensor. Of course, aspects of thepresent disclosure are not limited to the aforementioned sensors asother types of sensors (e.g., thermal, sonar, and/or lasers) are alsocontemplated for either of the first sensor 306 or the second sensor304.

The measurements of the first sensor 306 and the second sensor 304 maybe processed by the processor 320, the sensor module 302, the vehiclebehavior controller 310, the communication module 324, the plannermodule 326, the locomotion module 328, the onboard unit 330, and/or thelocation module 340. In conjunction with the computer-readable medium322, the measurements of the first sensor 306 and the second sensor 304are processed to implement the functionality described herein. In oneconfiguration, the data captured by the first sensor 306 and the secondsensor 304 may be transmitted to a connected vehicle via the transceiver332. The first sensor 306 and the second sensor 304 may be coupled tothe car 350 or may be in communication with the car 350.

The location module 340 may determine a location of the car 350. Forexample, the location module 340 may use a global positioning system(GPS) to determine the location of the car 350. The location module 340may implement a dedicated short-range communication (DSRC)-compliant GPSunit. A DSRC-compliant GPS unit includes hardware and software to makethe car 350 and/or the location module 340 compliant with one or more ofthe following DSRC standards, including any derivative or fork thereof:EN 12253:2004 Dedicated Short-Range Communication—Physical layer usingmicrowave at 5.8 GHz (review); EN 12795:2002 Dedicated Short-RangeCommunication (DSRC)—DSRC Data link layer: Medium Access and LogicalLink Control (review); EN 12834:2002 Dedicated Short-RangeCommunication—Application layer (review); EN 13372:2004 DedicatedShort-Range Communication (DSRC)—DSRC profiles for RTTT applications(review); and EN ISO 14906:2004 Electronic Fee Collection—Applicationinterface.

The communication module 324 may facilitate communications via thetransceiver 332. For example, the communication module 324 may beconfigured to provide communication capabilities via different wirelessprotocols, such as 5G, WiFi, long term evolution (LTE), 4G, 3G, etc. Thecommunication module 324 may also communicate with other components ofthe car 350 that are not modules of the vehicle behavior control system300. The transceiver 332 may be a communications channel through anetwork access point 360. The communications channel may include DSRC,LTE, LTE-D2D, mmWave, WiFi (infrastructure mode), WiFi (ad-hoc mode),visible light communication, TV white space communication, satellitecommunication, full-duplex wireless communications, or any otherwireless communications protocol such as those mentioned herein.

In some configurations, the network access point 360 includes Bluetooth®communication networks or a cellular communications network for sendingand receiving data including via short messaging service (SMS),multimedia messaging service (MMS), hypertext transfer protocol (HTTP),direct data connection, wireless application protocol (WAP), e-mail,DSRC, full-duplex wireless communications, mmWave, WiFi (infrastructuremode), WiFi (ad-hoc mode), visible light communication, TV white spacecommunication, and satellite communication. The network access point 360may also include a mobile data network that may include 3G, 4G, 5G, LTE,LTE-V2X, LTE-D2D, VoLTE, or any other mobile data network or combinationof mobile data networks. Further, the network access point 360 mayinclude one or more IEEE 802.11 wireless networks.

The vehicle behavior control system 300 also includes the planner module326 for planning a route and controlling the locomotion of the car 350,via the locomotion module 328 for autonomous operation of the car 350.In one configuration, the planner module 326 may override a user inputwhen the user input is expected (e.g., predicted) to cause a collisionaccording to an autonomous level of the car 350. The modules may besoftware modules running in the processor 320, resident/stored in thecomputer-readable medium 322, and/or hardware modules coupled to theprocessor 320, or some combination thereof.

The National Highway Traffic Safety Administration (“NHTSA”) has defineddifferent “levels” of autonomous vehicles (e.g., Level 0, Level 1, Level2, Level 3, Level 4, and Level 5). For example, if an autonomous vehiclehas a higher level number than another autonomous vehicle (e.g., Level 3is a higher level number than Levels 2 or 1), then the autonomousvehicle with a higher level number offers a greater combination andquantity of autonomous features relative to the vehicle with the lowerlevel number. These different levels of autonomous vehicles aredescribed briefly below.

Level 0: In a Level 0 vehicle, the set of advanced driver assistancesystem (ADAS) features installed in a vehicle provide no vehiclecontrol, but may issue warnings to the driver of the vehicle. A vehiclewhich is Level 0 is not an autonomous or semi-autonomous vehicle.

Level 1: In a Level 1 vehicle, the driver is ready to take drivingcontrol of the autonomous vehicle at any time. The set of ADAS featuresinstalled in the autonomous vehicle may provide autonomous features suchas: adaptive cruise control (“ACC”); parking assistance with automatedsteering; and lane keeping assistance (“LKA”) type II, in anycombination.

Level 2: In a Level 2 vehicle, the driver is obliged to detect objectsand events in the roadway environment and respond if the set of ADASfeatures installed in the autonomous vehicle fail to respond properly(based on the driver's subjective judgement). The set of ADAS featuresinstalled in the autonomous vehicle may include accelerating, braking,and steering. In a Level 2 vehicle, the set of ADAS features installedin the autonomous vehicle can deactivate immediately upon takeover bythe driver.

Level 3: In a Level 3 ADAS vehicle, within known, limited environments(such as freeways), the driver can safely turn their attention away fromdriving tasks, but must still be prepared to take control of theautonomous vehicle when needed.

Level 4: In a Level 4 vehicle, the set of ADAS features installed in theautonomous vehicle can control the autonomous vehicle in all but a fewenvironments, such as severe weather. The driver of the Level 4 vehicleenables the automated system (which is comprised of the set of ADASfeatures installed in the vehicle) only when it is safe to do so. Whenthe automated Level 4 vehicle is enabled, driver attention is notrequired for the autonomous vehicle to operate safely and consistentwithin accepted norms.

Level 5: In a Level 5 vehicle, other than setting the destination andstarting the system, no human intervention is involved. The automatedsystem can drive to any location where it is legal to drive and make itsown decision (which may vary based on the jurisdiction where the vehicleis located).

A highly autonomous vehicle (“HAV”) is an autonomous vehicle that isLevel 3 or higher. Accordingly, in some configurations the car 350 isone of the following: a Level 1 autonomous vehicle; a Level 2 autonomousvehicle; a Level 3 autonomous vehicle; a Level 4 autonomous vehicle; aLevel 5 autonomous vehicle; and an HAV.

The vehicle behavior controller 310 may be in communication with thesensor module 302, the processor 320, the computer-readable medium 322,the communication module 324, the planner module 326, the locomotionmodule 328, the location module 340, the onboard unit 330, and thetransceiver 332. In one configuration, the vehicle behavior controller310 receives sensor data from the sensor module 302. The sensor module302 may receive the sensor data from the first sensor 306 and the secondsensor 304. According to aspects of the disclosure, the sensor module302 may filter the data to remove noise, encode the data, decode thedata, merge the data, extract frames, or perform other functions. In analternate configuration, the vehicle behavior controller 310 may receivesensor data directly from the first sensor 306 and the second sensor 304to determine, for example, input traffic data images.

As shown in FIG. 3, the vehicle behavior controller 310 includes amobile unit communication module 312, a vehicle perception module 314,an V2V traffic state sharing module 316, and an vehicle controlselection module 318. The mobile unit communication module 312, thevehicle perception module 314, the V2V traffic state sharing module 316,and the vehicle control selection module 318 may be components of a sameor different artificial neural network, such as a deep convolutionalneural network (CNN). The vehicle behavior controller 310 is not limitedto a CNN. The vehicle behavior controller 310 receives a data streamfrom the first sensor 306 and/or the second sensor 304. The data streammay include a 2D RGB image from the first sensor 306 and LiDAR datapoints from the second sensor 304. The data stream may include multipleframes, such as image frames of traffic data.

The mobile unit communication module 312 may be configured tocommunicate with other connected vehicles within proximity of the car todetermine traffic state information, such as temporal traffic stateinformation. For example, a merging behavior of the car 350 may becontrolled by the vehicle behavior controller 310 in a manner that seeksto maximize an exposure time in order for an ego vehicle to make a morefluid entry into a merge gap between vehicles in a stream of traffic,for example, as shown in FIG. 4.

FIG. 4 is a diagram illustrating an overview of a highway environment,including vehicles on a highway main-lane and a controlled ego vehicleon a highway on-ramp, according to aspects of the present disclosure.The highway environment 400 includes a highway main-lane 410 havingvehicles 402, and a highway on-ramp 420 having a controlled ego vehicle450. In this configuration, the controlled ego vehicle 450 is configuredto monitor the dynamics of both vehicles on the highway main-lane 410,as well as vehicles on the highway on-ramp 420. In this example, thecontrolled ego vehicle 450, may be the car 350, shown in FIG. 3.

In one aspect of the present disclosure, the controlled ego vehicle 450is essentially controlled by a vehicle controller (e.g., the vehiclebehavior controller 310). In this example, the controlled ego vehicle450 (e.g., the vehicle perception module 314) identifies a merge gap 430between a first vehicle 452 and a second vehicle 454 on a first highwaymain-lane 412. That is, the controlled ego vehicle 450 is configured toidentify the merge gap 430 to enable entry onto the first highwaymain-lane 412 from on the highway on-ramp 420. According to aspects ofthe present disclosure, the controlled ego vehicle 450 is configured tocompute an exposure time in which the controlled ego vehicle 450 isspecified to merge into the merge gap 430.

In aspects of the present disclosure, the controlled ego vehicle 450 isconfigured to perform vehicle control actions to maximize the exposuretime. Maximizing the exposure time increases the probability of asuccessful merge onto the highway main-lane 410. In operation, thevehicle controller (e.g., vehicle control selection module 318) adjustsa speed of the controlled ego vehicle 450 to efficiently and smoothlymerge into traffic on the highway main-lane 410 from the highway on-ramp420. In this example, if a target speed of the controlled ego vehicle450 is not safe, the vehicle controller ignores the target speed andselects a different merge gap. Therefore, a collision avoidance functionis provided by the vehicle controller (e.g., vehicle behavior controller310).

In this example, the controlled ego vehicle 450 enters the highwayon-ramp 420 at fifty (50) kilometers (km) per hour (km/h), while theflow traffic of the vehicles 402 is approximately eighty (80) km/h.Subsequently, the speed of the controlled ego vehicle 450 is controlledmaximize the exposure time associated transitioning into the merge gap430. The vehicle controller controls the speed of the controlled egovehicle 450 until the controlled ego vehicle 450 successfully mergesonto the highway main-lane 410. In this example, the first vehicle 452and the second vehicle 454 may be connected vehicles configured tocommunicate with the controlled ego vehicle 450 to enable maximizing theexposure time to enter into the merge gap 430 and onto the highwaymain-lane 410.

FIG. 5 is a diagram illustrating an overview of a traffic environment,including vehicles on highway lanes and a controlled ego vehicle priorto a lane change, according to aspects of the present disclosure. Thetraffic environment includes a multilane highway 500 (e.g., a two lanehighway), having a first lane 502 and a target lane 504 (e.g., a secondlane), in which the first lane 502 includes a controlled ego vehicle550. In this configuration, the controlled ego vehicle 550 is configuredto monitor the dynamics of vehicles on the multilane highway 500, suchas a first vehicle 510 and a second vehicle 520 in the target lane 504of the multilane highway 500. In this example, the controlled egovehicle 550 desired to changes lanes from the first lane 502 to thetarget lane 504 of the multilane highway 500. In this example, thecontrolled ego vehicle 550, may be the car 350, shown in FIG. 3.

In one aspect of the present disclosure, the controlled ego vehicle 550is controlled by a vehicle controller (e.g., the vehicle behaviorcontroller 310). In this example, the controlled ego vehicle 550 (e.g.,the vehicle perception module 314) identifies a merge gap 530 betweenthe first vehicle 510 and the second vehicle 520 in the target lane 504.That is, the controlled ego vehicle 550 is configured to identify themerge gap 530 to enable a lane change of the controlled ego vehicle 550from the first lane 502 to the target lane 504. According to aspects ofthe present disclosure, the controlled ego vehicle 550 is configured tocompute an exposure time in which the controlled ego vehicle 550 isspecified to merge into the merge gap 530.

As further illustrated in FIG. 5, an S-axis 540 indicates a positionalong the target lane 504 of the multilane highway 500. In this example,the controlled ego vehicle 550 is shown at a position S_(e). Theposition S_(e) can change with time “t”, so it becomes a functionS_(e)(t). The same holds for the first vehicle 510 and the secondvehicle 520 that define the merge gap 530. The first vehicle 510 (e.g.,rear obstacle) has position S_(r)(t) and the second vehicle 520 (e.g.,front obstacle) has position S_(f)(t). The positions S_(r) and S_(f) canbe chosen to incorporated a predetermined amount of padding distance toaccount for the safe driving distance and the length of the controlledego vehicle 550.

According to aspects of the present disclosure, an exposure interval isdefined as an interval [T_(s), T_(e)] (e.g., start and end times). Forexample, the exposure interval is defined, such that a position of thecontrolled ego vehicle 550 is between the first vehicle 510 (e.g., rearvehicle) and the second vehicle (e.g., front vehicle), formally:

t _(e) >t _(s) ∧s _(r)(t)≤s _(e)(t)≤s _(f)(t), ∀t∈[t _(s) ,t _(e)]  (1)

In addition, the exposure time “E” is simply the length of the exposureinterval (e.g., T_(e)−T_(s)). Because a speed profile of the controlledego vehicle 550 is controlled and adjustable, control is also providedover the function S_(e)(t). According to one aspect of the presentdisclosure, a set of safe and comfortable acceleration/breaking profiles“A” are defined. In addition, variations of an exposure time arecomputed based on application of different acceleration/braking values.According to this aspect of the present disclosure, the exposure time isdefined as the largest exposure time that can be achieved with a safeand comfortable acceleration/braking value:

E=max_(a∈A)(t _(e) −t _(s)),

s.t.

t _(e) >t _(s)

s _(t)(t)≤s _(e)(t,a)≤s _(f)(t), ∀t∈[t _(s) ,t _(e)]  (2)

In aspects of the present disclosure, the controlled ego vehicle 550 isconfigured to perform vehicle control actions to maximize the exposuretime E according to Equation (2). Maximizing the exposure time increasesthe probability of a successful lane change within the multilane highway500. In operation, the vehicle controller (e.g., vehicle controlselection module 318) adjusts an acceleration/breaking of the controlledego vehicle 550 to efficiently and smoothly merge from the first lane502 to the target lane 504. In this example, if a targetacceleration/breaking value of the controlled ego vehicle 550 is notsafe, the vehicle controller ignores the target acceleration/breakingvalue and selects a different merge gap. Therefore, a collisionavoidance function is provided by the vehicle controller (e.g., vehiclebehavior controller 310).

FIG. 6 is a flowchart illustrating a method of improving a mergingefficiency of an ego vehicle, according to aspects of the presentdisclosure. A method 600 begins at block 602, in which one or more mergegaps are determined between vehicles in a target lane of a multilanehighway. For example, as shown in FIG. 5, the controlled ego vehicle 550identifies a merge gap 530 between the first vehicle 510 and the secondvehicle 520 in the target lane 504. That is, the controlled ego vehicle550 is configured to identify the merge gap 530 to enable a lane changeof the controlled ego vehicle 550 from the first lane 502 to the targetlane 504.

At block 604, an exposure time in which an ego vehicle is specified tomerge into the one or merge gaps is computed. For example, as shown inFIG. 5, the controlled ego vehicle 550 is configured to compute anexposure time in which the controlled ego vehicle 550 is specified tomerge into the merge gap 530. For example, the exposure interval isdefined, such that a position of the controlled ego vehicle 550 isbetween the first vehicle 510 (e.g., rear vehicle) and the secondvehicle (e.g., front vehicle). At block 606, a merge gap is selectedbetween a first vehicle and a second vehicle the target lane of themultilane highway having a maximum exposure time.

Maximizing the exposure time increases the probability of a successfullane change within the multilane highway 500. This process includesadjusting a selected acceleration/breaking value of the ego vehicle foreach of the one or merge gaps to maximize the exposure time for each ofthe one or more merge gaps. This process further includes selecting amerge gap from the one or more merge gaps according to a correspondingexposure time.

At optional block 608, a vehicle control action is selected to merge theego vehicle into the merge gap between the first vehicle and the secondvehicle in the target lane. Fox example, as shown in FIG. 5, thecontrolled ego vehicle 550 is configured to vary vehicle control actionsto maximize the exposure time E according to Equation (2). In operation,the vehicle controller (e.g., vehicle control selection module 318)adjusts an acceleration/breaking of the controlled ego vehicle 550 toefficiently and smoothly merge from the first lane 502 to the targetlane 504. The selecting of the merge gap in block 606 may also includediscarding a merge gap from the one or more merge gaps if a trigger timeto initiate merging into the discarded merge gap is greater than apredetermined value (e.g., ten seconds). Alternatively, if the exposuretime “E” is less than a predetermined threshold value (e.g. fourseconds) a is discarded merge gap from the one or more merge gaps.

In some aspects, the method shown in FIG. 6 may be performed by the SOC100 (FIG. 1) or the software architecture 200 (FIG. 2) of the autonomousvehicle 150. That is, each of the elements or methods may, for example,but without limitation, be performed by the SOC 100, the softwarearchitecture 200, the processor (e.g., CPU 102) and/or other componentsincluded therein of the autonomous vehicle 150, or the vehicle behaviorcontrol system 300.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining, and thelike. Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory), and thelike. Furthermore, “determining” may include resolving, selecting,choosing, establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules, and circuits describedin connection with the present disclosure may be implemented orperformed with a processor configured according to the presentdisclosure, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array signal (FPGA)or other programmable logic device (PLD), discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. The processor may be amicroprocessor, but, in the alternative, the processor may be anycommercially available processor, controller, microcontroller, or statemachine specially configured as described herein. A processor may alsobe implemented as a combination of computing devices, e.g., acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM, and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may connect a network adapter, amongother things, to the processing system via the bus. The network adaptermay implement signal processing functions. For certain aspects, a userinterface (e.g., keypad, display, mouse, joystick, etc.) may also beconnected to the bus. The bus may also link various other circuits suchas timing sources, peripherals, voltage regulators, power managementcircuits, and the like, which are well known in the art, and therefore,will not be described any further.

The processor may be responsible for managing the bus and processing,including the execution of software stored on the machine-readablemedia. Examples of processors that may be specially configured accordingto the present disclosure include microprocessors, microcontrollers, DSPprocessors, and other circuitry that can execute software. Softwareshall be construed broadly to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.Machine-readable media may include, by way of example, random accessmemory (RAM), flash memory, read only memory (ROM), programmableread-only memory (PROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), registers,magnetic disks, optical disks, hard drives, or any other suitablestorage medium, or any combination thereof. The machine-readable mediamay be embodied in a computer-program product. The computer-programproduct may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or specialized register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured with one or more microprocessorsproviding the processor functionality and external memory providing atleast a portion of the machine-readable media, all linked together withother supporting circuitry through an external bus architecture.Alternatively, the processing system may comprise one or moreneuromorphic processors for implementing the neuron models and models ofneural systems described herein. As another alternative, the processingsystem may be implemented with an application specific integratedcircuit (ASIC) with the processor, the bus interface, the userinterface, supporting circuitry, and at least a portion of themachine-readable media integrated into a single chip, or with one ormore field programmable gate arrays (FPGAs), programmable logic devices(PLDs), controllers, state machines, gated logic, discrete hardwarecomponents, or any other suitable circuitry, or any combination ofcircuits that can perform the various functions described throughout thepresent disclosure. Those skilled in the art will recognize how best toimplement the described functionality for the processing systemdepending on the particular application and the overall designconstraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a specialpurpose register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a non-transitorycomputer-readable medium. Computer-readable media include both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage medium may be any available medium that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.Additionally, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared (IR), radio, and microwave, then the coaxial cable,fiber optic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, include compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray®disc, where disks usually reproduce data magnetically, while discsreproduce data optically with lasers. Thus, in some aspectscomputer-readable media may comprise non-transitory computer-readablemedia (e.g., tangible media). In addition, for other aspects,computer-readable media may comprise transitory computer-readable media(e.g., a signal). Combinations of the above should also be includedwithin the scope of computer-readable media.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method of improving a merging efficiency of anego vehicle, the method comprising: determining one or more merge gapsbetween vehicles in a target lane of a multilane highway; computing anexposure time in which the ego vehicle is specified to merge into theone or merge gaps; and selecting a merge gap between a first vehicle anda second vehicle in the target lane of the multilane highway having amaximum exposure time.
 2. The method of claim 1, further comprising:selecting a vehicle control action to merge the ego vehicle into themerge gap between the first vehicle and the second vehicle in the targetlane of the multilane highway.
 3. The method of claim 2, in which thevehicle control action comprises accelerating a speed of the ego vehicleto successfully merge into the target lane of the multilane highway. 4.The method of claim 2, in which the vehicle control action comprisesdecelerating a speed of the ego vehicle to successfully merge into thetarget lane of the multilane highway.
 5. The method of claim 2, in whichthe vehicle control action comprises maintaining a speed of the egovehicle to successfully merge into the target lane of the multilanehighway.
 6. The method of claim 1, in which computing the exposure timecomprises: determining a position and an acceleration value of the firstvehicle and the second vehicle to define the merge gap; and selecting anacceleration/breaking value of the ego vehicle to maximize the exposuretime according to the merge gap between the first vehicle and the secondvehicle.
 7. The method of claim 6, in which the determining the positionand the acceleration value of the first vehicle and the second vehicleis performed by using vehicle-to-vehicle (V2V) communication between theego vehicle and the first vehicle and the second vehicle.
 8. The methodof claim 1, in which the ego vehicle in on an onramp of the multilanehighway and the target lane is a first lane of the multilane highway. 9.The method of claim 1, further comprising: selecting anacceleration/breaking value of the ego vehicle for each of the one ormerge gaps to determine the exposure time for each of the one or moremerge gaps; adjusting a selected acceleration/breaking value of the egovehicle for each of the one or merge gaps to maximize the exposure timefor each of the one or more merge gaps; and selecting a merge gap fromthe one or more merge gaps according to a corresponding exposure time.10. The method of claim 9, further comprising discarding a merge gapfrom the one or more merge gaps if a trigger time for the merge gap isgreater than a predetermined value or if the exposure time is less thana predetermined threshold value.
 11. A non-transitory computer-readablemedium having program code recorded thereon for improving a mergingefficiency of an ego vehicle, the program code being executed by aprocessor and comprising: program code to determine one or more mergegaps between vehicles in a target lane of a multilane highway; programcode to compute an exposure time in which the ego vehicle is specifiedto merge into the one or merge gaps; and program code to select a mergegap between a first vehicle and a second vehicle in the target lane ofthe multilane highway having a maximum exposure time.
 12. Thenon-transitory computer-readable medium of claim 11, further comprisingprogram code to select a vehicle control action to merge the ego vehicleinto the merge gap between the first vehicle and the second vehicle inthe target lane of the multilane highway.
 13. The non-transitorycomputer-readable medium of claim 11, in which the program code tocompute the exposure time comprises: program code to determine aposition and an acceleration value of the first vehicle and the secondvehicle to define the merge gap; and program code to select anacceleration/breaking value of the ego vehicle to maximize the exposuretime according to the merge gap between the first vehicle and the secondvehicle.
 14. The non-transitory computer-readable medium of claim 11,further comprising: program code to select an acceleration/breakingvalue of the ego vehicle for each of the one or merge gaps to determinethe exposure time for each of the one or more merge gaps; program codeto adjust a selected acceleration/breaking value of the ego vehicle foreach of the one or merge gaps to maximize the exposure time for each ofthe one or more merge gaps; and program code to select a merge gap fromthe one or more merge gaps according to a corresponding exposure time.15. The non-transitory computer-readable medium of claim 14, furthercomprising program code to discard a merge gap from the one or moremerge gaps if a trigger time for the merge gap is greater than apredetermined value or if the exposure time is less than a predeterminedthreshold value.
 16. A system to improve merging efficiency of an egovehicle, the system comprising: a vehicle perception module, including aconvolutional neural network configured to determine one or more mergegaps between vehicles in a target lane of a multilane highway; and acontroller module configured to compute an exposure time in which theego vehicle is specified to merge into one of the one or merge gaps, andto select a merge gap between a first vehicle and a second vehicle inthe target lane of the multilane highway having a maximum exposure time.17. The system of claim 16, further comprising a planner moduleconfigured to select a vehicle control action to merge the ego vehicleinto the merge gap between the first vehicle and the second vehicle inthe target lane of the multilane highway.
 18. The system of claim 17, inwhich the controller module is further configured to perform the vehiclecontrol action comprising accelerating a speed of the ego vehicle tosuccessfully merge into the target lane of the multilane highway. 19.The system of claim 17, in which the controller module is furtherconfigured to perform the vehicle control action comprising deceleratinga speed of the ego vehicle to successfully merge into the target lane ofthe multilane highway.
 20. The system of claim 17, in which in which thecontroller module is further configured to perform the vehicle controlaction comprising maintaining a speed of the ego vehicle to successfullymerge into the target lane of the multilane highway.